Papers › FasterSTS: A Faster Spatio-Temporal Synchronous Graph Convolutional Networks for...
FasterSTS: A Faster Spatio-Temporal Synchronous Graph Convolutional Networks for Traffic flow Forecasting
Ben-Ao Dai, Nengchao Lyu, Yongchao Miao
Accurate traffic flow prediction heavily relies on the spatio-temporal correlation of traffic flow data. Most current studies separately capture correlations in spatial and temporal dimensions, making it difficult to capture complex spatio-temporal heterogeneity, and often at the expense of increasing model complexity to improve prediction accuracy. Although there have been groundbreaking attempts in the field of spatio-temporal synchronous modeling, significant limitations remain in terms of performance and complexity control.This study proposes a quicker and more effective spatio-temporal synchronous traffic flow forecast model to address these issues.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Traffic Prediction | PeMS04 | FasterSTS | 12 Steps MAE | 18.49 | #9 of 12 | Archive leaderboard | report |
| Traffic Prediction | PeMS08 | FasterSTS | MAE@1h | 13.60 | #10 of 13 | Archive leaderboard | report |
| Traffic Prediction | PeMSD4 | FasterSTS | 12 steps MAE | 18.49 | #9 of 13 | Archive leaderboard | report |
| Traffic Prediction | PeMSD8 | FasterSTS | 12 steps MAE | 13.60 | #9 of 13 | Archive leaderboard | report |
| Traffic Prediction | PeMSD8 | FasterSTS | MAE@1h | 13.60 | #9 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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